import os
import sys
import json

sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'

import cv2
import copy
import numpy as np
import time
import tqdm
from PIL import Image
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.predict_cls as predict_cls
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from tools.infer.utility import draw_ocr_box_txt

logger = get_logger()


class TextSystem(object):
    def __init__(self, args):
        self.text_detector = predict_det.TextDetector(args)
        self.text_recognizer = predict_rec.TextRecognizer(args)
        self.use_angle_cls = args.use_angle_cls
        self.drop_score = args.drop_score
        if self.use_angle_cls:
            self.text_classifier = predict_cls.TextClassifier(args)

    def get_rotate_crop_image(self, img, points):
        '''
        img_height, img_width = img.shape[0:2]
        left = int(np.min(points[:, 0]))
        right = int(np.max(points[:, 0]))
        top = int(np.min(points[:, 1]))
        bottom = int(np.max(points[:, 1]))
        img_crop = img[top:bottom, left:right, :].copy()
        points[:, 0] = points[:, 0] - left
        points[:, 1] = points[:, 1] - top
        '''
        img_crop_width = int(
            max(
                np.linalg.norm(points[0] - points[1]),
                np.linalg.norm(points[2] - points[3])))
        img_crop_height = int(
            max(
                np.linalg.norm(points[0] - points[3]),
                np.linalg.norm(points[1] - points[2])))
        pts_std = np.float32([[0, 0], [img_crop_width, 0],
                              [img_crop_width, img_crop_height],
                              [0, img_crop_height]])
        M = cv2.getPerspectiveTransform(points, pts_std)
        dst_img = cv2.warpPerspective(
            img,
            M, (img_crop_width, img_crop_height),
            borderMode=cv2.BORDER_REPLICATE,
            flags=cv2.INTER_CUBIC)
        dst_img_height, dst_img_width = dst_img.shape[0:2]
        if dst_img_height * 1.0 / dst_img_width >= 1.5:
            dst_img = np.rot90(dst_img)
        return dst_img

    def print_draw_crop_rec_res(self, img_crop_list, rec_res):
        bbox_num = len(img_crop_list)
        for bno in range(bbox_num):
            cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
            logger.info(bno, rec_res[bno])

    def __call__(self, img):
        ori_im = img.copy()
        dt_boxes, elapse = self.text_detector(img)
        logger.info("dt_boxes num : {}, elapse : {}".format( len(dt_boxes), elapse))
        if dt_boxes is None:
            return None, None
        img_crop_list = []

        dt_boxes = sorted_boxes(dt_boxes)

        for bno in range(len(dt_boxes)):
            tmp_box = copy.deepcopy(dt_boxes[bno])
            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
            img_crop_list.append(img_crop)
        if self.use_angle_cls:
            img_crop_list, angle_list, elapse = self.text_classifier(
                img_crop_list)
            logger.info("cls num  : {}, elapse : {}".format(len(img_crop_list), elapse))

        rec_res, elapse = self.text_recognizer(img_crop_list)
        logger.info("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
        filter_boxes, filter_rec_res = [], []
        for box, rec_reuslt in zip(dt_boxes, rec_res):
            text, score = rec_reuslt
            if score >= self.drop_score:
                filter_boxes.append(box)
                filter_rec_res.append(rec_reuslt)
        return filter_boxes, filter_rec_res


def sorted_boxes(dt_boxes):
    """
    Sort text boxes in order from top to bottom, left to right
    args:
        dt_boxes(array):detected text boxes with shape [4, 2]
    return:
        sorted boxes(array) with shape [4, 2]
    """
    num_boxes = dt_boxes.shape[0]
    sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
    _boxes = list(sorted_boxes)

    for i in range(num_boxes - 1):
        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
            tmp = _boxes[i]
            _boxes[i] = _boxes[i + 1]
            _boxes[i + 1] = tmp
    return _boxes

def read_file_list(fname):
    file_list = []
    with open(fname, 'r', encoding='utf8') as fh:
        for line in fh.readlines():
            file_list.append(line.strip())
    return file_list


def main(args):
    file_lists = args.file_list
    save_dir = args.save_dir
    img_dir = args.image_dir
    os.makedirs(save_dir, exist_ok=True)
    text_sys = TextSystem(args)

    for filename in file_lists:
        files = read_file_list(filename)
        out_file = os.path.join(save_dir, os.path.splitext(os.path.basename(filename))[0] + '.json')
        results = {}
        for name in files:
            img = cv2.imread(os.path.join(img_dir, name), cv2.IMREAD_COLOR)
            dt_boxes, rec_res = text_sys(img)
            key = os.path.splitext(name)[0]
            results[key] = {}
            results[key]['pointsList'] = []
            results[key]['transcriptionsList'] = []
            results[key]['ignoreList'] = []
            results[key]['classesList'] = []

            for box, (text, score) in zip(dt_boxes, rec_res):
                results[key]['pointsList'].append([float(x) for x in list(box.reshape(-1))])
                results[key]['transcriptionsList'].append(text)
                results[key]['ignoreList'].append(False)
                results[key]['classesList'].append(1)
        
        json.dump(results, open(out_file, 'w', encoding='utf8'), ensure_ascii=False)


if __name__ == "__main__":
    args = utility.parse_args()
    project_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

    # args.image_dir = "F:/Data/tianchiOCR/test"
    # args.save_dir = "F:/Data/tianchiOCR/results"
    # args.file_list = "F:/Data/tianchiOCR/Xeon1OCR_round1_test3_20210528.txt"
    # args.det_model_dir = "F:/Data/models/paddle/ch_ppocr_mobile_v2.0_det_infer"
    # args.cls_model_dir = "F:/Data/models/paddle/ch_ppocr_mobile_v2.0_cls_infer"
    # args.rec_model_dir = "F:/Data/models/paddle/ch_ppocr_mobile_v2.0_rec_infer"
    # args.rec_char_dict_path = os.path.join(project_dir, "ppocr/utils/ppocr_keys_v1.txt")

    main(args)
